Robotic information gathering has many applications in fundamental research (e.g. oceanography, geology, biology) and in resource extraction (e.g. mining, agriculture, forestry). The state of practice is for humans to oversee robotic operations, often with monitoring burdens that can have significant costs. Remote operations are plagued by restrictions on communications: bandwidth limits, latency, and drop out. There is a need to develop systems for scientific data collection that operate autonomously with high neglect tolerance.
Building trustworthy autonomous systems hinges on a multitude of complex factors, such as humans’ ability to communicate their actual objectives to robotic systems, robots’ ability to explain their behaviour to remote operators, and users’ previous experience with deployed robots. Automated science operations demand more than just specialist training with robots. They demand a fundamental understanding of (and new technologies to address) difficult problems, such as Explainable AI and Competency Aware reasoning, given the non-obvious requirements for correct behaviour.
This workshop will bring together roboticists and scientists to identify what roboticists need to understand about science objectives, as well as what capabilities roboticists can provide to achieve those objectives. We are looking for submissions that address: the problem of trusting autonomous data collection for short- and long-term operations, methods to elicit scientists’ preferences, and gaps between science needs and autonomous capabilities. The outcome of this workshop will be organized into a report documenting the major conclusions of the workshop and a potential special issue in a major robotics journal (if selected by the journal editorial boards).
UPDATE: The RSS Conference will be Virtual in response to the COVID-19 pandemic, this workshop will follow suit and also be virtual. We will operate the main event through a Zoom meeting, and accepted posters will be presented through individual zoom meetings. We have also pushed back the submission deadline to 22 May, 2020.
We are looking for 2-page abstracts in PDF form. Selected submissions will be presented as posters during the interactive sessions of the workshop.
Abstract Submission Deadline: 22 May 2020 Anywhere on Earth
Notification for Contributors: 19 June 2020
Submission Process: [submissions now closed]
Date: 13 July, 2020
Time: 0815hrs to 1530hrs
Location: Corvallis, Oregon, Virtually
E-Mail: explainable.data.collection@gmail.com
Dr. Julie A. Adams, Professor, Associate Director of the Collaborative Robotics and Intelligent Systems Institute, Oregon State University. Dr. Adams was the founder of the Human-Machine Teaming Laboratory at Vanderbilt University, prior to moving the laboratory to Oregon State. Adams has worked in the area of human-machine teaming for thirty years. Throughout her career she has focused on human interaction with unmanned systems, but also focused on manned civilian and military aircraft at Honeywell, Inc. and commercial, consumer and industrial systems at the Eastman Kodak Company. Her research, which is grounded in robotics applications for domains such as first response, archaeology, oceanography, the national airspace, and the U.S. military, focuses on distributed artificial intelligence, swarms, robotics and human-machine teaming. Dr. Adams received her M.S. and Ph.D. degrees in Computer and Information Sciences from the University of Pennsylvania and her B.S. in Computer Science and B.B.E. in Accounting from Siena College.
Dr. Timothy Chung joined DARPA’s Tactical Technology Office as a program manager in February 2016. He serves as the Program Manager for the OFFensive Swarm-Enabled Tactics Program and the DARPA Subterranean (SubT) Challenge. His interests include autonomous/unmanned air vehicles, collaborative autonomy for unmanned swarm system capabilities, distributed perception, distributed decision-making, and counter unmanned system technologies.
Prior to joining DARPA, Dr. Chung served as an Assistant Professor at the Naval Postgraduate School and Director of the Advanced Robotic Systems Engineering Laboratory (ARSENL). His academic interests included modeling, analysis, and systems engineering of operational settings involving unmanned systems, combining collaborative autonomy development efforts with an extensive live-fly field experimentation program for swarm and counter-swarm unmanned system tactics and associated technologies. Dr. Chung also served as Deputy Director of the Secretary of the Navy initiative for the Consortium for Robotics and Unmanned Systems Education and Research (CRUSER).
Dr. Chung holds a Bachelor of Science in Mechanical and Aerospace Engineering from Cornell University. He also earned Master of Science and Doctor of Philosophy degrees in Mechanical Engineering from the California Institute of Technology.
Alan Fern is a Professor of Computer Science at Oregon State University. His general research area is artificial intelligence with an emphasis on machine learning, automated planning, and the intersection of those areas. He is particularly interested in developing model-based planning systems that can learn from experience and humans, as well as explain their decisions to humans. He is an associate editor of Machine Learning and the Journal of Artificial Intelligence Research and is regularly an area chair for ICML, NeurIPS, and AAAI.
Dr. Eric W. Frew is a professor in the Ann and H.J. Smead Aerospace Engineering Sciences Department and Director of the Autonomous Systems Interdisciplinary Research Theme (ASIRT) at the University of Colorado Boulder (CU). He received his B.S. in mechanical engineering from Cornell University in 1995 and his M.S and Ph.D. in aeronautics and astronautics from Stanford University in 1996 and 2003, respectively. Dr. Frew has been designing and deploying unmanned aircraft systems for over twenty years. His research efforts focus on autonomous flight of heterogeneous unmanned aircraft systems; distributed information-gathering by mobile robots; miniature self-deploying systems; and guidance and control of unmanned aircraft in complex atmospheric phenomena. Dr. Frew was co-leader of the team that performed the first-ever sampling of a severe supercell thunderstorm by an unmanned aircraft. He is currently the CU Site Director for the National Science Foundation Industry / University Cooperative Research Center (IUCRC) for Unmanned Aircraft Systems. He received the NSF Faculty Early Career Development (CAREER) Award in 2009 and was selected for the 2010 DARPA Computer Science Study Group.
Yogesh Girdhar is a computer scientist, and the PI of the WARPLab (http://warp.whoi.edu) at Woods Hole Oceanographic Institution (WHOI), and an Associate Scientist (without Tenure) in the Applied Ocean Physics & Engineering department. He received his BS and MS from Rensselaer Polytechnic Institute in Troy, NY; and his Ph.D. from McGill University in Montreal, Canada. During his Ph.D. Girdhar developed an interest in ocean exploration using autonomous underwater vehicles, which motivated him to come to WHOI, initially as a postdoc, and then later continue as a scientist to start WARPLab. Girhdar’s research has since then focused on developing smarter autonomous exploration robots that can accelerate the scientific discovery process in extreme and challenging environments, such as the deep sea. Some notable recognition of his work includes the Best Paper Award in Service Robotics at ICRA2020, finalist for Best Paper Award at IROS 2018, and honorable mention for 2014 CIPPRS Doctoral Dissertation Award.
Dr. David P. Miller has been the Wilkonson Chair and Professor of Intelligent Systems based in the School of AME at the University of Oklahoma since 1999. Dr. Miller has a Bachelors in Astronomy from Wesleyan University and a PhD in Computer Science/AI from Yale. His primary research areas are in robot mobility, the tradeoff between algorithm and mechanism, assistive technology and STEM education. Miller worked at NASA’s Ames Research Center and the Jet Propulsion Laboratory, and was awarded the NASA Exceptional Service Medal in 1993 for his work at JPL on small rovers, leading to the Mars Pathfinder Rover Mission. He is a founder of KISS Institute for Practical Robotics and its Botball Program. Miller has been the faculty advisor of the OU Boomer Rocket Team and the Sooner Rover Team (SoRo). He teaches courses in programming, space science and astrodynamics, as well as a variety of courses (both lecture and laboratory) in robotics. He is currently on a rotation at the US National Science Foundation (NSF) on loan from the University of Oklahoma.
Dr. Joshua Peschel is an Assistant Professor of Agricultural and Biosystems Engineering and Black & Veatch Faculty Fellow at Iowa State University; he also holds courtesy appointments in the departments of Electrical and Computer Engineering and Civil, Construction and Environmental Engineering. Dr. Peschel conducts research in the area of cyber-agricultural systems where he creates new technologies, data sets, and computational models for sensing and sensemaking. His research has been supported by the National Science Foundation, U.S. Departments of Agriculture, Defense and Energy, the Bill & Melinda Gates Foundation, and a number of commodity groups and private industry partners.
Ufuk Topcu joined the Department of Aerospace Engineering at the University of Texas at Austin as an assistant professor in Fall 2015. He received his Ph.D. degree from the University of California at Berkeley in 2008. He held research positions at the University of Pennsylvania and California Institute of Technology. His research focuses on the theoretical, algorithmic, and computational aspects of design and verification of autonomous systems through novel connections between formal methods, learning theory, and controls.
Dr. Kiri L. Wagstaff is a principal researcher in machine learning at NASA's Jet Propulsion Laboratory and an associate research professor at Oregon State University. Her research focuses on developing new machine learning and data analysis methods for use onboard spacecraft and in data archives for planetary science, astronomy, cosmology, and more. She holds a Ph.D. in Computer Science from Cornell University followed by an M.S. in Geological Sciences from the University of Southern California and a Master of Library and Information Science (MLIS) from San Jose State University. She received a 2008 Lew Allen Award for Excellence in Research for work on the sensitivity of machine learning methods to high-radiation space environments and a 2012 NASA Exceptional Technology Achievement award for work on transient signal detection methods in radio astronomy data. She also served as a Tactical Uplink Lead (operational planning) for the Mars Opportunity rover. She is passionate about keeping machine learning relevant to real-world problems.
Explainable AI
Machine Self-Confidence
Formal Validation and Verification
Environmental monitoring
Planetary exploration
Search and rescue
Disaster recovery
Expert preference elicitation
Interpretable models of decision making
Hypothesis-based reasoning and hypothesis generation
Nisar Ahmed, University Colorado, Boulder
P. Michael Furlong, NASA Ames Intelligent Robotics Group [KBR, Inc.]
Geoff Hollinger, Oregon State University
Seth McCammon, Oregon State University